Mobile data transmission and data services are constantly making progress, wherein such services provide various communication services, such as voice, video, packet data, messaging, broadcast, etc. In recent years, Long Term Evolution LTE™ has been specified, which uses the Evolved Universal Terrestrial Radio Access Network E-UTRAN as radio communication architecture according to 3GPP specification.
In the course of LTE evolution, the use of Coordinated Multipoint CoMP operation, i.e. transmission/reception, has been proposed, so as to improve coverage and throughput. One approach of such CoMP operation is Multi-cell coordinated scheduling MuCCS, wherein the scheme thereof is a coordinated scheduling CS based CoMP technique that dynamically mutes cells on Radio Bands RBs based on a network-wide sum utility maximization framework.
However, the set of cells over which such joint optimization is done is usually pre-determined. For example, 3GPP has defined in the respective specifications thereof a 3-cell (intra-site) cooperation cluster and a 9-cell hard partitioned cooperating cluster. That is, any given cell in the network is part of at most one cluster of cells, and no two clusters overlap.
But this optimization over hard-partitioned cell clusters is not ideal, as not all of a user equipment's UE strong interferers may be taken into account when doing the joint optimization. Instead, it has been proposed in MuCCS to use overlapping “liquid” clusters for a distributed architecture implementation.
A “liquid” cluster is defined in a cell-specific manner, as the set of all neighboring cells that are strong interferers of some UE in that cell. Further, the fraction of UEs whose strong interferer is within the Liquid Cluster of their serving cells would thus be 100%. The “liquid” cluster of any one cell would in general be different from the “liquid” cluster of every other cell, and the “liquid” clusters of two cells may overlap.
A UE's CoMP measurement set which is a UE-specific set of cooperating cells is determined from out of the UE's serving cell's liquid cluster. Clusters are usually formed in a UE-centric or network-centric manner.
In UE-centric clustering, each UE chooses a small number of cells that give the greatest cooperation gain. In general, UE-centric clustering is, however, very complex from a scheduling point of view. Each base station eNB locally comes up with its own set of candidate “cooperating cluster” eNBs based on the UE measurements like Reference Signal Receive Power RSRP, Channel Quality Indicator CQI, etc.
In network-centric clustering the clustering is done in a static way, and hence the performance of boundary UEs can be compromised.
However, the above mentioned clustering approaches do not take into account practical limitations e.g. a non-ideal backhaul. In real networks, the cell locations/sizes are irregular, the load conditions are also very dynamic and the backhaul connections are also non-ideal, such as due to some backhaul delay and finite capacity. So, mechanisms are required to adaptively determine the appropriate “liquid cluster” of a cell that provides the maximum MuCCS gains taking the above factors into account.
Hence, in view of the above drawbacks, there is a need for improving cell clustering for Coordinated Multipoint operation, in particular for adaptively forming “liquid clusters” for a cell.